title: Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.
abstract: This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.
authors.forename: Hideaki
authors.initials: H
authors.lastname: Hayashi
authors.forename: Keisuke
authors.initials: K
authors.lastname: Shima
authors.forename: Taro
authors.initials: T
authors.lastname: Shibanoki
authors.forename: Yuichi
authors.initials: Y
authors.lastname: Kurita
authors.forename: Toshio
authors.initials: T
authors.lastname: Tsuji
mesh_terms: D000465:Algorithms; D016002:Discriminant Analysis; D004569:Electroencephalography; D006801:Humans; D008390:Markov Chains; D016571:Neural Networks, Computer; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors